Supervisory Control Strategy Research Articles

Performance analysis of small capacity absorption chillers by using different modeling methods

This paper presents a review and comparison of simple, yet accurate steady-state models of small capacity absorption chillers using highly reliable experimental data obtained with an absorption chiller of 12 kW in a state-of-the-art test bench. These models can potentially be used in complete modeling and simulation tools or in supervisory control strategies for air-conditioning systems using absorption chillers.With respect to that, a comparative evaluation of different modeling methods for predicting the absorption chiller performance is presented. Four empirically based models: the adapted Gordon-Ng model (GNA), the characteristic equation model (ΔΔt′), the multivariable polynomial model (MPR) and the artificial neural networks model (ANN) were applied using the experimental data and thoroughly examined. The paper also presents statistical indicators and tests which might assist in selection of the most appropriate model.The excellent statistical indicators such as coefficient of determination (>0.99) and coefficient of variation (<5%) clearly indicate that it is possible to develop highly accurate empirical models by using only the variables of external water circuits as model input parameters.

Real-Time Optimal Energy Management of Heavy Duty Hybrid Electric Vehicles

The performance of energy flow management strategies is essential for the success of hybrid electric vehicles (HEVs), which are considered amongst the most promising solutions for improving fuel economy as well as reducing exhaust emissions. The heavy duty HEVs engaged in cycles characterized by start-stop configuration has attracted widely interests, especially in off-road applications. In this paper, a fuzzy equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for heavy duty HEVs. The online optimization problem is formulated as minimizing a cost function, in terms of weighted fuel power and electrical power. A fuzzy rule-based approach is applied on the weight tuning within the cost function, with respect to the variations of the battery state-of-charge (SOC) and elapsed time. Comparing with traditional real-time supervisory control strategies, the proposed F-ECMS is more robust to the test environments with rapid dynamics. The proposed method is validated via simulation under two transient test cycles, with the fuel economy benefits of 4.43% and 6.44%, respectively. The F-ECMS shows better performance than the telemetry ECMS (T-ECMS), in terms of the sustainability of battery SOC.

Simulation of a stand-alone residential PEMFC power system with sodium borohydride as hydrogen source

Catalytic hydrolysis of sodium borohydride (NaBH4) has been investigated as a method to generate hydrogen for fuel cell applications. The high purity of the generated hydrogen makes this process a potential source of hydrogen for polymer electrolyte membrane fuel cells (PEMFCs). In this paper, a PEMFC power system employing a NaBH4 hydrogen generator is designed to supply continuous power to residential power applications as stand-alone loads and simulated using Matlab/Simulink software package. The overall system is sized to meet a real end-use load, representative of standard European domestic medium electric energy consumption, over a 1-week period. Supervisory control strategies are proposed to manage the hydrogen generation and storage, and the power flow. Simulation results show that the proposed supervisory control strategies are effective and the NaBH4–PEMFC power system is a technologically feasible solution for stand-alone residential applications.

Hierarchical safety supervisory control strategy for robot-assisted rehabilitation exercise

SUMMARYClinical outcomes have shown that robot-assisted rehabilitation is potential of enhancing quantification of therapeutic process for patients with stroke. During robotic rehabilitation exercise, the assistive robot must guarantee subject's safety in emergency situations, e.g., sudden spasm or twitch, abruptly severe tremor, etc. This paper presents a hierarchical control strategy, which is proposed to improve the safety and robustness of the rehabilitation system. The proposed hierarchical architecture is composed of two main components: a high-level safety supervisory controller (SSC) and low-level position-based impedance controller (PBIC). The high-level SSC is used to automatically regulate the desired force for a reasonable disturbance or timely put the emergency mode into service according to the evaluated physical state of training impaired limb (PSTIL) to achieve safety and robustness. The low-level PBIC is implemented to achieve compliance between the robotic end-effector and the impaired limb during the robot-assisted rehabilitation training. The results of preliminary experiments demonstrate the effectiveness and potentiality of the proposed method for achieving safety and robustness of the rehabilitation robot.

Advances in developing supervisory control strategies for flotation plants

The wide plant control integration of mineral processes poses many challenges. Flotation plants consist of complex interacting circuits where minerals are processed in different stages with recycling. Today, a common arrange are the RCS circuits, where a rougher circuit is combined with a cleaning circuit and a scavenger circuit. The global objectives of a flotation plant are to maximize the value metal recovery while the grade of the final concentrate is kept inside a narrow band. To achieve this, for any time variant feed attributes (flow rate, solid percent, grade, particle size distribution, pH, and chemicals), a capability of modifying the operation of each circuit, in order to achieve some local objectives, is demanded. To advance in this study, a family of simulators can be of great help. Recently, an algorithm to set the froth level profile in a rougher circuit, have been proposed. The simulator used was based on phenomenological models with parameters estimated from industrial data, collected under designed experiments. In this work, a simulator for the cleaning and scavenger circuits, including a regrinding stage is presented. This simulator is based on phenomenological models, with parameters estimated from industrial data. The simulators are used to provide insight on how the available resources in each circuit can be administrated, to obtain some local objectives which are harmonized in a global control strategy. Copyright© 2013 IFAC

Intelligence-Based Supervisory Control for Optimal Operation of a DCS-Controlled Grinding System

Optimizing the final grinding production indices (GPIs), which include the product particle size and the grinding production rate, to meet the overall manufacturing performance requirements is the main function of automatic control of a grinding circuit (GC). However, the complex and time-varying nature of the GC process dictates that these GPIs cannot be optimized solely by the lower-level distributed control systems (DCS), therefore an operator is often incorporated to manually determine the set-points for the DCS using his/her operational experience. With a human being involved, the performance and even the safety and stability of the GC operation is subject to human errors. Focusing on this practical challenge, this paper proposes an intelligence-based supervisory control strategy that consists of a control loop set-point optimization module, an artificial neural network-based soft-sensor module, a fuzzy logic-based dynamic adjustor, and an expert-based overload diagnosis and adjustment module to perform the control tasks for the GC system. This hybrid system can automatically adjust the set-points for the DCS-controlled grinding system in response to the changes in boundary conditions or the imminent overload conditions, thereby eliminating the need for an operator. Practical applications have shown the validity and effectiveness of the proposed approach.

Supervisory control design for systems of multiple sources of energy

This paper describes a supervisory control strategy for electrical energy transfers in multisource renewable energy systems. The sources are coupled onto a DC bus through DC/DC power converters. The aim is to control the energy transfers, according to the sources power and load variations. The controller determines the operating mode of the system. Then, it calculates the power ratio provided by each source and drives the DC/DC power converters with local current and voltage loops in order to regulate the voltage on the DC bus according to a reference value. The main contributions are to use the duty cycle values of the DC/DC power converters as decision criteria to switch the power sources and drive the power ratios, and to present the complete strategy in a single hierarchical control scheme with three stages. A non linear model of the closed loop system is also detailed in order to work out sufficient conditions for asymptotic stability. Finally, the proposed control scheme is validated with an experimental device developed by GREAH Research Group for the control of energy transfers in multi-source renewable energy systems.

Development of supervisory control strategy for optimized fuel consumption of the compound hybrid excavator

This paper describes a hybrid supervisory control strategy for fuel optimization of a compound hybrid excavator that incorporates an engine-assist motor, an electric swing motor and a super capacitor in the original drive train. The main features of the compound hybrid excavator are that the electrically-propelled swing motor increases the number of energy paths and also incurs many constraints related to the power balance of the hybrid drive train. The dynamic programming technique has been applied to the constrained nonlinear fuel optimization problem over representative excavation cycles. Then, by imitating the behaviour of the dynamic programming optimization scheme, a rule-based controller has been designed. The rule-based controller determines the diesel engine set speed and the engine-assist motor power to operate the engine in an efficient region and to minimize the energy loss in the hybrid drive train. The performance of the rule-based controller has been compared to that of the thermostat controller, which determines the energy distribution, only based on the state of charge of the applied super capacitor. Simulation results indicate that the compound hybrid excavator can improve overall fuel efficiency by about 20% compared to the conventional excavator for representative excavation cycles, and the proposed rule-based controller further enhances the fuel economy by about 2–3% compared to the simple thermostat controller.

Transient control for micro-grid with multiple distributed generations based on hybrid system theory

With the rapid increase in the rate of distributed generation (DG) penetration depth, the issues of improving micro-grid transient become more significant. This paper investigates a two-level hierarchical hybrid control consists of continuous local controller for each DG unit at the first level coordinated by discrete supervisory control strategies at the secondary level for transients performance enhancement of micro-grid with various DG units following pre-planned or accidental events. The discrete supervisory control strategies are established based on information fusion technique by using wide-area measurements (WAMs) in order to switch each DG subsystem into apposite operational mode following large disturbances. The continuous local controller for each DG unit is designed based on multiple Lyapunov stability theory integrating linear matrix inequality (LMI) techniques in order to regulate the set point of each DG subsystem to reach the best performance and acceptable operation indexes. The effectiveness of the proposed hybrid control is demonstrated through simulation examples.

Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building

Rule extraction is a promising technique for developing or fine-tuning supervisory control strategies in buildings. Three data mining techniques are examined that extract rules from offline model predictive control (MPC) results for a mixed mode building operated during the cooling season: generalized linear models (GLM), classification and regression trees (CART), and adaptive boosting. All rules were able to recover approximately 90% of the original optimizer energy savings under open loop tests, but the GLM-based rules saw significant performance degradation under simulated tests. CART and boost rules only degraded in performance by a few percentage points, still retaining the vast majority of optimizer savings (84% and 93% for the CART and boost rules, respectively). The results demonstrate that the proposed rule extraction techniques may allow building automation systems to achieve near-optimal supervisory control strategies without online MPC systems, although further research is required to broadly test applicability to more complex cases.

Fault-tolerant supervisory control of building condenser cooling water systems for energy efficiency

This article presents a fault-tolerant supervisory control strategy for building condenser cooling water systems. The proposed strategy mainly consists of a model-based predictive control (MPC) scheme, a fault detection and diagnosis (FDD) scheme and a fault accommodation and tolerant (FAT) scheme. The MPC scheme using systematic optimization is employed to identify optimal control settings for the local process controllers. The FDD scheme is utilized to detect and diagnose major possible faults that may happen in the routine operation of condenser cooling water systems. The faults considered mainly include critical sensor faults, physical component performance degradations, and malfunctions of control logics. According to the types of faults that happen, the FAT scheme is then used to handle the faults in order to regain the control as far as possible. The performance of this strategy is tested and evaluated in a simulated virtual system representing the actual condenser cooling water system in a super high-rise building. The results show that the proposed strategy is capable of maintaining acceptable control performance and can help save about 0.18%–5.23% total energy of the chillers and cooling towers when the operation of condenser cooling water systems suffers from some faults, as compared to that using the same control strategy but without using the FAT scheme.

H∞ Based Supervisory Control Strategy for A Parallel HEV with Battery Fault Accommodation

This paper presents a robust fault-tolerant H∞ approach for the development of the energy management strategy (EMS) for a parallel hybrid electric vehicle (PHEV). The objective is to determine the power split ratio between engine and battery that maintains the state of energy (SOE) of the battery within a reasonable range to prevent the battery from undesirable breakdown under the situation of existence of battery fault. The system model which consists of the engine part; the electric motor part and the battery part (here a equivalent circuit model) is given first. Then the control system structure with weighting functions, taking into account the battery fault and the control signal limit, is given. Finally, the controller is derived and some simulations for a specific PHEV are included.

Real-time Safety Control of Upper-limb Rehabilitation Robot

A real-time online safety supervisory-control strategy based on fuzzy logic is presented to improve the safety and stability for the upper-limb rehabilitation robot in clinic application.During the robot-aided impaired limb rehabilitation exercise,the impaired limb condition impacts the control performance.An intelligent safety supervisory fuzzy controller (SSFC) is designed to enhance the movement stability and safety in emergent condition.Firstly,the movement features are extracted to evaluate the stability of the impaired limb,subsequently the proposed safety supervisory fuzzy controller intelligently adapts the desired control force to a reasonable disturbance or responds to a sudden event in time.Secondly,a position-based impedance control strategy is adopted to achieve the compliance between the impaired limb and the robotic end-effector.Experimental results show the effectiveness of the proposed method for achieving the safety and stability of the rehabilitation robot.

A Novel ECMS and Combined Cost Map Approach for High-Efficiency Series Hybrid Electric Vehicles

The main aim in the control of a hybrid electric vehicle (HEV) is to decrease the fuel consumption and emissions without significant loss of driving performance. The performance of the vehicle in terms of fuel economy and emissions is very much dependent on the vehicle's supervisory control strategy. In this paper, the equivalent consumption minimization strategy (ECMS) is developed with a novel approach for the charge sustaining of the batteries to provide an overall improved optimization performance for series hybrid electric vehicles (SHEVs), considering the efficiencies of the internal combustion engine (ICE), generator, and battery. Another novelty is the development of a combined map, which simultaneously facilitates the optimization of the fuel consumption and multiple emission components, unlike most past studies that have concentrated on one component at a time. After the derivation of the cost map, the algorithm is divided into two main parts. The first part optimizes the engine-generator set (GENSET), and the second part determines how much power is needed from the GENSET according to the ECMS. The algorithm is implemented using generic emission and fuel consumption maps of an actual mid-sized series hybrid bus to reduce the desired emissions. The hybrid electric vehicle in consideration is converted from a conventional bus that is driven by an ICE. The performance of the novel ECMS strategy is compared with the conventional vehicle, as well as the SHEV version that is driven by an on-off strategy. In addition to reduced fuel consumption, the results of this paper demonstrate a significant reduction of 14.58% in CO2 production with ECMS, whereas the on-off control strategy achieves only 6.47% reduction over the conventional vehicle.

Constrained load/frequency control problems in networked multi-area power systems

In this paper, we present a supervisory discrete-time predictive control strategy for load/frequency control problems in networked multi-area power systems subject to coordination constraints. Coordination between the control center and the spatially distributed areas is accomplished via data networks subject to communication latency modeled by time-varying time-delay. The aim here is finding supervising strategies able to reconfigure, whenever necessary in response to unexpected load changes and/or faults, the nominal set-points on frequency and generated power to the generators of each area so that viable evolutions would arise for the overall power system and a new sustainable equilibrium is reached. In order to demonstrate the effectiveness of the strategy, examples on a four-area power system are presented.

Fuzzy logic and wavelet-based energy management strategy for fuel cell/ultracapacitor/battery hybrid vehicle with multiple-input DC/DC converter

Fuel cell/ultracapacitor/battery hybrid power systems represent a promising architecture to satisfy the energy requirements for road vehicles. When the primary power source is a fuel cell, the objective of an energy management strategy (EMS) is to optimise the hydrogen consumption and fuel cell efficiency without compromising the performance of the overall system. In this study, a fuzzy logic controller together with a wavelet-transform algorithm (WTA) is proposed as EMS, which is a reasonable option to achieve an adequate solution of the problem since this supervisory control strategy can deal with a considerable amount of variables. Besides, a WTA is capable of separating the high and low frequency components of the load profile and help allocate them among the on-board energy sources. A simulation environment has been developed to test the EMS; it includes models for the power sources and energy storage devices, the power electronics and the vehicular power demand.

ICONE19-43275 Studies on the Coordinated Operation and Autonomous Control for Multi-modular Nuclear Power Plants

The tendency has always been to build ever larger single-modular reactor plants with the objective of benefiting from economies of scale. These plants have compiled admirable safety records. Nevertheless, there is concern that conventional large single reactors have become too complex by reason of placing too much reliance on engineered safeguards. The multi-modular approach offers a solution in that its use of many small reactors in conjunction with several shared turbines permits a simpler core design while, at the same time, at least partially retaining economies of scale by increasing the number of modules. Specific advantages to the multi-modular approach are as follows. First, the small-sized of the reactor core may allow the incorporation of passive safety features such as natural circulation cooling on loss of off-site electricity. Second, the individual modules are to be sized so that components related to nuclear safety can be factory-fabricated. Moreover, once the major components are made, they are to be transported to the site for rapid installation. This construction method is expected to reduce the licensing effort because the modules will be pre-licensed, and only site-specific issues will have to be considered in the final licensing process. At present, related studies show that the multi-modular approach for Generation IV can retain both the inherent safety and good economies of scale. However, the unbalanced load operation of the multi-modular power plant in which each module operates at a different power level and strong coupling between multi modules creates a complex control challenge to safe operation and control. Firstly, this paper summarizes the unbalanced load operation characteristics and challenges faced by operation and control of multi-modular power plant in the dynamic operational characteristics and requirements of coordinated control between multi modules. Secondly, detailed analysis and comparison are given in the integral supervisory hierarchical control structure, operation and control strategy of existing multi-modular reactor in the world. Finally, this paper indicates that the high-performance autonomous control method is one of the possible research directions to solve the problems of operation and control of multi-modular power plant.

An experimental and analytical comparison study of power management methodologies of fuel cell–battery hybrid vehicles

The implementation of fuel cell vehicles requires a supervisory control strategy that manages the power distribution between the fuel cell and the energy storage device. Some of the current problems with power management strategies are: fuel efficiency optimization methods require prior knowledge of the driving cycle before they can be implemented, the impact on the fuel cell and battery life cycle are not considered and finally, there are no standardized measures to evaluate the performance of different control methods. In addition to that, the performances of different control methods for power management have not been directly compared using the same mathematical models. The proposed work will present a different optimization approach that uses fuel mass flow rate instead of fuel mass consumption as the cost function and thus, it can be done instantaneously and does not require knowledge of the driving cycle ahead of time. Also this study presents an experimental approach to validate the mathematical simulation results.

Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm

This paper presents a model-based supervisory and optimal control strategy for central chiller plants to enhance their energy efficiency and control performance. The optimal strategy is formulated using simplified models of major components and the genetic algorithm (GA). The simplified models are used as the performance predictors to estimate the system energy performance and response to the changes of control settings and working conditions. Since the accuracy of the models has significant impacts on the overall prediction results, the models used are linear in the parameters and the recursive least squares (RLS) estimation technique with exponential forgetting is used to identify and update the model parameters online. That is to ensure that the linear models can provide reliable and accurate estimates when working condition changes. The GA, as a global optimization tool, is used to solve the optimization problem and search for globally optimal control settings. The performance of this strategy is tested and evaluated in a simulated virtual system representing the actual central chiller plant in a super high-rise building under various working conditions. The results showed that this strategy can save about 0.73–2.55% daily energy of the system studied, as compared to a reference strategy using conventional settings.

Equivalent fuel consumption optimal control of a series hybrid electric vehicle

Improvements in hybrid electric vehicle fuel economy with reduced emissions strongly depend on their supervisory control strategy. In order to develop an efficient real-time supervisory control strategy for a series hybrid electric bus, the proposed equivalent fuel consumption optimal control strategy is compared with two popular strategies, thermostat and power follower, using backward simulations in ADVISOR. For given driving cycles, global optimal solutions were also obtained using dynamic programming to provide an optimization target for comparison purposes. Comparison simulations showed that the thermostat control strategy optimizes the operation of the internal combustion engine and the power follower control strategy minimizes the battery charging and discharging operations which, hence, reduces battery power loss and extends the battery life. The equivalent fuel consumption optimal control strategy proposed in this paper provides an overall system optimization between the internal combustion engine and battery efficiencies, leading to the best fuel economy.